The field of the invention generally relates to an apparatus and method for controlling a system affected by stochastic variables and, in particular, to a nutrient control system for nourishing plants.
In agriculture many management decisions are repetitive and made in the context of limited resources, with limited knowledge of possible future events. These decisions are important to the growth and nurturing of crops because actions taken early in the life of a plant can profoundly affect its health and total yield. Plant growth management expertise results from years of cultivating living plants, and decisions are influenced by experience garnered over the years.
There exists a need to be able to coordinate many parameters, both objective and subjective, in order to make decisions which satisfy minimum standards of all variables. The effects of some decisions may remain unknown for a long time and thus systems of control through immediate feedback become impossible. Often the outcomes can only be estimated, but there exists the ability to make reasonable predictions based upon the expertise of people knowledgeable in the area. Computerized decision making which benefits from this human expertise is desirable.
Horticultural production, a prime example of controlled environment agriculture, could usefully incorporate the concepts and practicalities of computerized decision making. Consideration of all of the management options required to be evaluated in a horticultural operation is an enormous task. Competition in the production of horticultural crops continues to intensify. Planting, variety selection, fertilization, pest management, disease and weed control and harvest and delivery have been examined. As a result, the adoption of more advanced techniques of decision analysis, artificial intelligence and expert systems as possible solutions have been proposed.
The science of decision analysis has been assembled over many years to analyze various outcomes of decision options in order to conclude which of the available options would be the most advantageous. The construction of decision trees and the use of probabilities and utility functions to arrive at an optimal decision out of a set of options has been used for scenarios from investments to fire fighting to the siting of the Mexico airport. The methodology has been established and the Bayesian view of probability is straightforward and mathematically sound.
Decision analysis necessitates the derivation of the probabilities, outcomes and/or utilities that are required to construct a decision tree. The computer science world, in its research into artificial intelligence, has introduced a computer programming approach commonly referred to as an inference engine which could be used to give probabilities, outcomes, and utilities. An inference engine is an algorithm used with knowledge bases to infer conclusions. Such a system comprising an engine and knowledge base is called an expert system, as the knowledge based files are usually constructed from the knowledge of a person or persons having expertise in the area of concern.
In analyzing the respective roles played by experts and decision analysts, it was concluded that the expertise gained through years of experience by an expert could be referred to as "soft logic". It is not founded in math, but it could be perfectly sound, and it would be subject to manipulation by changes of evidence.
Conversely, the science of decision analysis could be referred to as "hard logic." Decision analysis was founded in math with very specific theorems and proofs. Varying inputs could produce varying outputs, but the means by which those outputs were computed remained the same.
There is also a need for a model combining decision analysis, with its mathematics or "hard logic" approach, and expert systems, with their intuitive expertise or "soft logic" so that consistent, repetitive, short-term decisions could be made. There is also a need for a model that could be assembled in computer memory in such a way that a computer could make such a "decision" and act upon it. There is also a need for such a model to make operational decisions regarding the control of systems. There is also a need for such a model to be built and implemented to control systems such as a controlled environment or a greenhouse, so that the grower is free to make tactical decisions rather than operational decisions.
Operational decisions made on the production of plants grown in a controlled environment should incorporate considerations of incoming solar radiation, heating and ventilating, carbon dioxide levels, irrigation and nutrient supply and the possibility of disease and/or pests. The production of the plants depends on maintaining the growth parameters, temperature, relative humidity, carbon dioxide concentration, radiation, and water and nutrient supply, within predefined limits to achieve acceptable growth, output and quality. These limits vary depending upon the maturity of the plant, the type of plant that is being grown, the environmental conditions and the solar radiation available.
Some of the parameters change value quickly and some change slowly. Temperature and relative humidity effects of heating and cooling the air in a controlled environment could be evident within seconds, particularly close to heating pipes. Plant uptake of water and nutrients have time constants of the order of minutes. Intermittent solar energy fluctuations can have time constants of minutes, but major radiation fluctuations take place diurnally. Finally, the item which is the most important, the output, takes weeks or months to reach maturity and then fluctuates in production from one day to the next.
In order to operate such a decision model successfully, there is a need to identify the controllable variables, the stochastic variables and the indices of performance.